mindspore.Tensor.reshape

Tensor.reshape(*shape) → Tensor

Rearranges self Tensor based on the given shape.

The shape can only have one -1 at most, in which case it's inferred from the remaining dimensions and the number of elements in self Tensor.

Parameters

shape (Union[tuple[int], list[int], Tensor[int]]) – If shape is a tuple or list, its elements should be integers, and only constant value is allowed. i.e., $(y_{1}, y_{2}, . . ., y_{S})$ . If shape is a Tensor, data type should be int32 or int64, and only one-dimensional tensor is supported.

Returns

Tensor, If the given shape does not contain -1, the shape of tensor is $(y_{1}, y_{2}, . . ., y_{S})$ . If the k-th position in the given shape is -1, the shape of tensor is $(y_{1}, . . ., y_{k - 1}, \frac{\prod_{i = 1}^{R} x_{i}}{y_{1} \times . . . \times y_{k - 1} \times y_{k + 1} \times . . . \times y_{S}}, y_{k + 1}, . . ., y_{S})$

Raises

ValueError – The given shape contains more than one -1.
ValueError – The given shape contains elements less than -1.
ValueError – For scenarios where the given shape does not contain -1, the product of elements of the given shape is not equal to the product of self tensor's shape, $\prod_{i = 1}^{R} x_{i} \neq \prod_{i = 1}^{S} y_{i}$ , (Namely, it does not match self tensor's array size). And for scenarios where the given shape contains -1, the product of elements other than -1 of the given shape is an aliquant part of the product of self tensor's shape $\prod_{i = 1}^{R} x_{i}$ .

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> output = Tensor.reshape(input, (3, 2))
>>> print(output)
[[-0.1  0.3]
 [ 3.6  0.4]
 [ 0.5 -3.2]]